How to Use the ImageCharts MCP in AutoGen

Q: How do I register the ImageCharts MCP Server tools with an AutoGen AssistantAgent?

You use mcpservertools with the Vinkius HTTP transport params to fetch the tool definitions. Then, pass the resulting tool list directly to your AutoGen AssistantAgent constructor.

Let your AutoGen agents debate data points and render the consensus as an AutoGen-driven chart.

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Create your Vinkius account to connect ImageCharts to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ImageCharts with AutoGen

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Multi-agent consensus visualized via MCP Server

The `generate_doughnut_chart` tool represents percentage allocations once your AutoGen agents reach a collaborative decision. After debating budget splits or resource limits, the coordinator agent calls this tool to lock in the final visual. This MCP Server execution ensures that the visual output matches the structured JSON consensus. No manual plotting is required after the debate terminates.

Debate and map complex overlaps in AutoGen

The `generate_venn_diagram` tool maps overlapping logical sets derived from multi-agent discussions. When a security agent and a developer agent negotiate feature scopes, they output their shared priorities to this tool. AutoGen routes the tool's returned image URL back into the group chat. This gives human operators immediate visual confirmation of what the agents agreed on, so there's no confusion.

Track multi-agent performance metrics dynamically

The `generate_bubble_chart` tool plots multi-dimensional agent performance metrics like speed, accuracy, and cost. A supervisor agent calls this tool to plot the efficiency of other agents in the conversation. The generated chart URL is shared in the chat session, allowing the system to log visual progress over long-running runs.

Setup guide

Set up ImageCharts MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes ImageCharts tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="ImageCharts_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent ImageCharts data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="ImageCharts_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent ImageCharts data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ImageCharts. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ImageCharts MCP in AutoGen

You define the system prompt to instruct your AutoGen coordinator agent to visualize quantitative data. When a debate concludes with structured numbers, the agent calls `generate_bar_chart` to output the final visual.

Yes. Because the MCP Server handles requests statelessly, different agents in your AutoGen group can call `generate_line_chart` or `generate_pie_chart` at the same time without conflicts.

You use mcp_server_tools with the Vinkius HTTP transport params to fetch the tool definitions. Then, pass the resulting tool list directly to your AutoGen AssistantAgent constructor.

The MCP tool adapter returns the raw API validation error to the calling agent. The AutoGen agent reads this feedback and self-corrects the parameter formatting in its next turn.

The tool parameters, including raw dataset arrays and labels, are transmitted directly via a secure V8 isolate sandbox. V8 enforces strict ephemeral execution, meaning your data points are never cached or written to persistent storage logs.

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